Predicting Herb-disease Associations Through Graph Convolutional
Network

Wang, Bing; He, Binsheng; Hu, Xuan; You, Long; Geng, Tao; Bing, Pingping

doi:10.2174/1574893618666230504143647

Cited by 3 publications

(2 citation statements)

References 74 publications

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“…Huang et al have proposed a network screening-based framework to identify a novel combination drug for heart failure treatment, while the method cannot discover the potential functional drugs effectively and deep association information. Hu et al have only used graph convolutional networks (GCNs) to predict the associations between herbs and diseases . Feng et al have developed a novel method combining an autoencoder, a transformer, and two-dimensional (2D) fingerprints for drug repositioning .…”

Section: Introductionmentioning

confidence: 99%

“…Hu et al have only used graph convolutional networks (GCNs) to predict the associations between herbs and diseases. 9 Feng et al have developed a novel method combining an autoencoder, a transformer, and two-dimensional (2D) fingerprints for drug repositioning. 10 Although existing studies have provided a large number of valuable data, including molecular properties 11 and knowledge graphs, 12 to lay the ground for the further mining and analysis of the computational models, there are still many challenges for screening more efficacious drugs.…”

Section: ■ Introductionmentioning

confidence: 99%

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SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution

Ren,

Yu,

et al. 2023

J. Chem. Inf. Model.

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Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug−disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

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Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution

Ren,

Yu,

et al. 2023

J. Chem. Inf. Model.

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Graph convolutional network learning model based on new integrated data of the protein-protein interaction network and network pharmacology for the prediction of herbal medicines effective for the treatment of metabolic diseases

Kim,

Yu,

Lim

et al. 2024

Preprint

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Chronic metabolic diseases constitute a group of conditions requiring long-term management and hold significant importance for national public health and medical care. Currently, in Korean medicine, there are no insurance-covered herbal prescriptions designated primarily for the treatment of metabolic diseases. Therefore, the objective of this study was to identify herbal prescriptions from the existing pool of insurance-covered options that could be effective in treating metabolic diseases. This research study employed a graph convolutional network learning model to analyze PPI network constructed from network pharmacology, aiming to identify suitable herbal prescriptions for various metabolic diseases, thus diverging from literature-based approaches based on classical indications. Additionally, the derived herbal medicine candidates were subjected to transfer learning on a model that binarily classified the marketed drugs into those currently used for metabolic diseases and those that are not for data-based verification. GCN, adept at capturing patterns within protein-protein interaction (PPI) networks, was utilized for classifying and learning the data. Moreover, gene scores related to the diseases were extracted from GeneCards and used as weights. Due to the absence of any prior research using similar data and learning structures, an alternative evaluation method of our pre-trained model was deemed necessary. The performance of the pre-trained model was validated through 5-fold cross-validation and bootstrapping with 100 iterations. Furthermore, to ascertain the superior performance of our proposed model, the number of layers was varied, and the performance of each was evaluated. Our proposed model structure achieved outstanding performance in classifying drugs, with an average precision of 96.68%, recall of 97.18%, and an F1 score of 96.74%. The trained model predicted that the most effective decoction would be Jowiseunggi-tang for hyperlipidemia, Saengmaegsan for hypertension, and Kalkunhaeki-tang for type 2 diabetes. This study is the first of its kind to integrate GCN with weighted PPI network data to classify herbal prescriptions by their potential for usage on certain diseases.

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Causal associations and shared genetic etiology of neurodegenerative diseases with epigenetic aging and human longevity

Guo,

Ma,

Wang

et al. 2024

Aging Cell

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The causative mechanisms underlying the genetic relationships of neurodegenerative diseases with epigenetic aging and human longevity remain obscure. We aimed to detect causal associations and shared genetic etiology of neurodegenerative diseases with epigenetic aging and human longevity. We obtained large‐scale genome‐wide association study summary statistics data for four measures of epigenetic age (GrimAge, PhenoAge, IEAA, and HannumAge) (N = 34,710), multivariate longevity (healthspan, lifespan, and exceptional longevity) (N = 1,349,462), and for multiple neurodegenerative diseases (N = 6618–482,730), including Lewy body dementia, Alzheimer's disease (AD), Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis. Main analyses were conducted using multiplicative random effects inverse‐variance weighted Mendelian randomization (MR), and conditional/conjunctional false discovery rate (cond/conjFDR) approach. Shared genomic loci were functionally characterized to gain biological understanding. Evidence showed that AD patients had 0.309 year less in exceptional longevity (IVW beta = −0.309, 95% CI: −0.38 to −0.24, p = 1.51E‐19). We also observed suggestively significant causal evidence between AD and GrimAge age acceleration (IVW beta = −0.10, 95% CI: −0.188 to −0.013, p = 0.02). Following the discovery of polygenic overlap, we identified rs78143120 as shared genomic locus between AD and GrimAge age acceleration, and rs12691088 between AD and exceptional longevity. Among these loci, rs78143120 was novel for AD. In conclusion, we observed that only AD had causal effects on epigenetic aging and human longevity, while other neurodegenerative diseases did not. The genetic overlap between them, with mixed effect directions, suggested complex shared genetic etiology and molecular mechanisms.

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Predicting Herb-disease Associations Through Graph Convolutional Network

Cited by 3 publications

References 74 publications

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution

Graph convolutional network learning model based on new integrated data of the protein-protein interaction network and network pharmacology for the prediction of herbal medicines effective for the treatment of metabolic diseases

Causal associations and shared genetic etiology of neurodegenerative diseases with epigenetic aging and human longevity

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